import sys
import os
import pandas as pd
import numpy as np
import logging
from datetime import datetime
from PyQt5.QtCore import QThread, pyqtSignal

logger = logging.getLogger(__name__)


class StockProcessor(QThread):
    data_fetched = pyqtSignal(pd.DataFrame)
    processing_started = pyqtSignal()
    processing_finished = pyqtSignal(list, dict)  # 关键点和状态数据
    key_points_ready = pyqtSignal(str)
    error_occurred = pyqtSignal(str)
    cache_updated = pyqtSignal(int, str, dict)  # stock_id, stock_name, state_data

    def __init__(self, cache_manager):
        super().__init__()
        self.cache_manager = cache_manager
        self.user_input = None
        self.period = None
        self.interval = None
        self.stock_name = ""

    def map_stock_code(self, user_input):
        """股票代码映射逻辑"""
        # 这里实现实际的映射逻辑
        return user_input, f"{user_input}股票"  # 简化处理

    def run(self):
        try:
            # 1. 获取数据
            # 这里实现数据获取逻辑
            logger.info(f"开始处理股票: {self.user_input}")

            # 模拟数据获取
            date_rng = pd.date_range(start='2023-01-01', end='2023-06-30', freq='D')
            data = pd.DataFrame({
                'Open': np.random.rand(len(date_rng)) * 100 + 100,
                'High': np.random.rand(len(date_rng)) * 10 + 105,
                'Low': np.random.rand(len(date_rng)) * 10 + 95,
                'Close': np.random.rand(len(date_rng)) * 10 + 100,
                'Volume': np.random.randint(100000, 1000000, size=len(date_rng))
            }, index=date_rng)

            self.data_fetched.emit(data)

            # 2. 处理数据
            self.processing_started.emit()

            # 模拟数据处理
            key_points = [
                {'Date': datetime(2023, 1, 10), 'point_type': '笔顶', 'price': 100.5},
                {'Date': datetime(2023, 1, 15), 'point_type': '笔底', 'price': 95.2},
                {'Date': datetime(2023, 1, 20), 'point_type': '笔顶', 'price': 102.3},
            ]

            state_data = {
                'current_fractal': '顶分型',
                'current_pen': '向上笔',
                'current_zs': '中枢形成中',
                'zs_high': 102.3,
                'zs_low': 95.2,
                'details': "当前处于中枢形成阶段，建议观望"
            }

            self.processing_finished.emit(key_points, state_data)

            # 3. 格式化关键点
            key_points_str = "\n".join([f"{kp['Date'].strftime('%Y-%m-%d')} {kp['point_type']} {kp['price']}"
                                        for kp in key_points])
            self.key_points_ready.emit(key_points_str)

            # 4. 更新缓存
            stock_id = self.cache_manager.get_stock_id(self.user_input, self.user_input, self.stock_name)
            self.cache_updated.emit(stock_id, self.stock_name, state_data)

        except Exception as e:
            logger.error(f"处理过程中出错: {str(e)}")
            self.error_occurred.emit(str(e))